The present invention relates generally to the field of natural language processing, and more particularly to term matching in question answering systems.
Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics that, amongst other things, is concerned with using computers to derive meaning from natural language text. NLP systems may perform many different tasks, including, but not limited to, determining the similarity between certain words and/or phrases. One known way to determine the similarity between words and/or phrases is to compare their respective word embeddings. A word embedding is a mapping of natural language text to a vector of real numbers in a continuous space (and is also sometimes referred to as a “vector representation”). Generally speaking, the word embeddings of similar words are located close to each other in the continuous space.
Question answering (QA) systems are computer systems that use NLP to answer questions posed by humans in natural language. Term matching is a QA process by which QA systems evaluate whether a given corpus (or “passage”) is relevant to answering a particular question.